The paper introduces the Register Anything Model (RAM), a unified approach for 3D point cloud registration that can robustly estimate 6‑DoF transformations across a wide range of domains, including indoor, outdoor, and object scenes. RAM overcomes domain‑specific challenges by employing a geometry‑aware normalization that voxelizes and standardizes point clouds based on their geometric properties, followed by a hierarchical coarse‑to‑fine pipeline with a geometric transformer that focuses on local consistency. Experiments on 12 benchmarks show that RAM achieves state‑of‑the‑art inlier ratios and registration recall, outperforming methods trained on individual domains and demonstrating strong generalization.
By Zheng Qin, Junhua Xi, Yan Huang, Jiahong Lai, Anwen Huang, Qiong Li, Guangda Zhang, Kai Xu
SAGE (Semantic-Augmented Geometric Experts) is a new point‑cloud registration method that incorporates frozen, pre‑trained semantic representations into the registration pipeline without extra sensors or fine‑tuning. It uses a Semantic‑Guided Mixture of Experts (SG‑MoE) to route data so that each expert specializes in specific semantic‑geometric patterns, thereby reducing mismatches that plague geometry‑only approaches. On the 3DLoMatch benchmark, SAGE achieves the highest registration recall and, with the same number of refinement iterations, also attains the best inlier ratio and recall on both 3DLoMatch and 3DMatch.
By Tomoyasu Shimada, Kazuhiko Murasaki, Shoichiro Takeda, Shogo Sato, Riku Inoue, Toshihiko Nishimura, Ryuichi Tanida
The paper introduces GMPCR, a non‑learning spectral consistency‑guided framework for multiview point cloud registration in low‑overlap scenes. GMPCR refines initial correspondences into a second‑order compatibility structure, uses spectral analysis to filter unreliable matches and select informative scan pairs, and then applies maximal‑clique hypothesis generation for robust relative transformations. The resulting sparse pose graph is further refined with an adaptive history‑aware synchronization scheme, and a recovery mechanism allows previously down‑weighted edges to regain confidence, achieving high registration recalls on benchmark datasets while reducing computational cost.
By Tianyu Li, Yanghong Lin, Shudong Zhou, Kui Yang, Jingru Zhang, Li Fang, Wei Yao
arXiv:2610.34271v1 Announce Type: new
Abstract: We propose a novel method for robust point cloud registration with only one tunable parameter. Our method, dubbed TLP, uses truncated $L_p$ functions w...
By Seong Hun Lee, Patrick Vandewalle, Javier Civera
arXiv:2608. 07564v1 Announce Type: cross Abstract: In digital dentistry and oral surgery, the registration of jawbone CT and intraoral scanner (IOS) data is essential for integrating internal bone structure with high-resolution dental surface geometry.
By Sho Mitarai, Hikaru Kayo, Hisashi Ozaki, Yuichiro Imai, Megumi Nakao
arXiv:2609.37071v1 Announce Type: new
Abstract: Non-rigid point-cloud registration aims to find the corresponding target point for each point on a deforming source surface. Point-level matching keeps...
By Yuzhen He, Sara Homscheid
arXiv:2609.25375v1 Announce Type: cross
Abstract: Global point-cloud registration remains challenging when limited overlap, repetitive geometry, and sensor noise produce correspondence sets dominated...
By Abolfazl Babanazari, Carson Cramer, Tyler Summers, Carlos Nieto, Kaveh Fathian
arXiv:2606. 27818v1 Announce Type: cross Abstract: We present MMD-Reg, a novel correspondence-free approach to point-cloud registration that is differentiable and has linear computational complexity in the number of points.
By Rixon Crane, Fahira Afzal Maken, Nicholas Lawrance, Stanislav Funiak, Kasra Khosoussi, Ming Xu, Russell Tsuchida
C3VDReg is a benchmark for local-to-local colonoscopic registration that uses the Colonoscopy 3D Video Dataset (C3VD) to generate 10,015 partial-to-partial point cloud pairs, with 2,088 held‑out test pairs. Each pair consists of a source point cloud from depth reprojection and a target point cloud from CT mesh raycasting, evaluated under a standardized protocol of 8,192 points per cloud and fixed pose conventions. Experiments show that high geometric overlap does not guarantee reliable pose recovery, revealing translation ambiguity along repetitive tubular anatomy as a key failure mode.
By Linzhe Jiang, Jiayuan Huang, Sophia Bano, Matthew J. Clarkson, Zhehua Mao, Mobarak I. Hoque
CUFormer is a modality-aware registration framework that aligns intraoperative ultrasound (US) bone-surface point clouds with preoperative computed tomography (CT) models, addressing challenges from differing imaging physics and partial surface coverage. It introduces a Modality-Aware Transformer (MAT) that encodes modality through identity embeddings and uses modality-specific adapters within a shared geometric matcher, while modeling imaging visibility to weight correspondences. On two CT-US datasets (UltraBones100k and UltraBonesHip) and the CT-RGB-D SpineDepth dataset, CUFormer achieves the lowest mean registration errors, notably reducing RMSE by 65.8% on UltraBones100k compared to the best baseline.
By Lixing Tan, Hongye Hao, Tianrun Gu, Qing Xia, Yuting Guo, Shuai Li, Aimin Hao
arXiv:2608.22131v1 Announce Type: cross
Abstract: Craniosynostosis severity analysis increasingly relies on statistical shape models (SSMs) to quantify cranial morphology, but most existing workflows...
By Sanjay Bhandari, Nawazish Khan, Alzbeta Novotna, Tiffany Jeong, Loretta Bowman, Michael Hernandez, Tobi Somorin, Viraj Govani, Jesse Goldstein, Shireen Elhabian
arXiv:2606. 10019v1 Announce Type: cross Abstract: We propose a fast and correspondence-free local point cloud registration method that leverages geometric surface structure and reproducing kernel Hilbert space (RKHS) embeddings.
By Ray Zhang, Marcus Greiff, Thomas Lew, John Subosits